Robust anatomy detection from CT topograms

We present an automatic method to quickly and accurately detect multiple anatomy region-of-interests (ROIs) from CT topogram images. Our method first detects a redundant and potentially erroneous set of local features. Their spatial configurations are captured by a set of local voting functions. Unlike all the existing methods where the idea was to try to "hit" the correct/best constellations of local features, we have taken an opposite approach. We try to peel away the bad features until a safe (i.e., conservatively small) number of features remain. It is deterministic in nature and guarantees a success even for extremely noisy cases. The advantages of the method are its robustness and computational efficiency. Our method also addresses the potential scenario in which outliers (i.e., false landmarks detections) forms plausible configurations. As long as such outliers are a minority, the method can successfully remove these outliers. The final ROI of the anatomy is computed from a best subset of the remaining local features. Experimental validation was carried out for multiple organs detection from a large collection of CT topogram images. Fast and highly robust performance was observed. In the testing data sets, the detection rate varies from 98.2% to 100% for different ROIs and the false detection rate is from 0.0% to 0.5% for different ROIs. The method is fast and accurate enough to be seamlessly integrated into a real-time work flow on the CT machine to improve efficiency, consistency, and repeatability.